Diagnosis of arrhythmias with few abnormal ECG samples using metric-based meta learning.

A major challenge in artificial intelligence based ECG diagnosis lies that it is difficult to obtain sufficient annotated training samples for each rhythm type, especially for rare diseases, which makes many approaches fail to achieve the desired performance with limited ECG records. In this paper, we propose a Meta Siamese Network (MSN) based on metric learning to achieve high accuracy for automatic ECG arrhythmias diagnosis with limited ECG records. First, the ECG signals from three different ECG datasets are preprocessed through resampling, wavelet denoising, R-wave localization, heartbeat segmentation and Z-score normalization.